Article Index

2200 Geniuses and a Self-Driving
Car - After the success of last
year's GPU Technology
Conference, we were
pretty excited to host our 2nd event in September this year. Our attendee numbers
grew more than 50%, well above average for a technical conference, and submissions
from eager CUDA developers wanting to present their work grew nearly 400%. In fact,
we had so many that we doubled the number of sessions at the conference to 280, all
of which are online
for your viewing and listening pleasure :)

It was pretty interesting to see the difference in the show since last year. The
sheer breadth of topics covered made the show unlike any other - from astrophysics
to video processing, from computational fluid dynamics to neuroscience and from
energy exploration to designing autonomous cars. Tables were filled with engineers,
scientists, developers, students and researchers, all sharing experiences and ideas.
We'll be staying in San Jose, California for GTC 2011, and we hope to see you all
there.

Here are a few of my favorite quotes from members of the press that attended:

"Absolutely one of the best - and most important conferences in the technology and
advanced computing sector" - The Exascale Report

"What we are seeing here is like going from propellers to jet engines." - insideHPC

"...GTC is growing even as it specializes on just one aspect of NVIDIA's business,
the CUDA platform for GPU computing. That's just one of many signals that point to
an undeniable trend: the use of GPUs for non-graphics computation is on the rise,
led largely by NVIDIA's efforts." - Tech Report

"NVIDIA's GTC is a blast. The demos, keynotes, exhibits, technical papers, and
emerging companies' presentations are first class, interesting and informative. Well
worth the price of admission. There was no heavy product messaging, no call to
action to buy something other than the idea that parallel processing is here and
it's important-and by our observations it was mission accomplished." - Tech Watch

{mosgoogle right}

Turbocharged Tools - This year we saw GPU-enabled, production releases of
some of the most important applications in the technical and scientific computing
space. ACUSIM Software launched a GPU-enabled
version of its CFD software AcuSolve, delivering double the
performance
for its users.

Tom Lange, director of Modeling and Simulation at P&G said:

"GPU-accelerated CFD allows for more realism, helping us replace slow and expensive
physical learning cycles with virtual ones. This transforms engineering analysis
from the study of failure to true virtual trial and error, and design optimization."

ANSYS
released
performance data on its CUDA implementation of ANSYS Mechanical, revealing that
CUDA helps cut turnaround times for complex simulations in
half. Wolfram Research
released
the latest version
of Mathematica, delivering for its users, in some cases, speed increases of more
than 100X from within the familiar confines of the Mathematica programming
environment. Check out the video here of their
demo
earlier this year at Siggraph. And finally, NVIDIA and
Mathworks collaborated on its
latest release of MATLAB 2010b, to include support for GPU
acceleration for
users of Parallel Computing
Toolbox and MATLAB
Distributed Computing Server.

Cloudy with a chance of GPUs - this year saw the first GPU deployments to
the
Cloud,
from Peer1 in July and Amazon Web Services
(AWS) in November. Developing for the CUDA architecture of NVIDIA GPUs already
offers the lowest cost of entry for any HPC architecture, but with these new
services, you don't even need to buy the hardware yourself. Through AWS for
example, you can now get access to 2 Tesla 20-series GPUs and 2 CPUs for just $2.10
an hour. Businesses of all sizes can now run heavy duty simulations and more with
simple on-demand pricing , and no large up front capital investment.
GigaOm Pro had
this
to say about the announcement:

"Performance (of Amazon's Cluster Compute Instances) was high already, and the
addition of GPUs just ups the octane level. According to a benchmark test by HPC
cloud-resource middleman Cycle Computing, GPU Instances outperform in-house GPU
clusters
in certain cases."

Lean, Mean & Green - The year ended with a bang for the Tesla business at
SC'10 in New Orleans. The final Top500 and Green500 lists of the year were announced
and Tesla had its best showing yet. Just prior to SC'10 commencing, the National
Supercomputer Center in Tianjin announced
Tianhe-1A
which, with a Linpack score of 2.57 petaflops, secured the #1 spot on the list. Two other Tesla
GPU-enabled systems
made the Top5; the aforementioned Nebulae, and Tsubame 2.0 from Tokyo Tech.

Tsubame 2.0 was ranked at #2 in the Green500, but more notably it was the only
petaflop system in the entire Top 10. Equipped with 4200 Tesla GPUs, yet consuming
just 1.340 megawatts, it is, by far, the most power efficient petaflop system the
world has ever seen ands an incredible achievement from Prof. Satoshi Matsuoka and
his team.

NVIDIA and its customers were also recognized in a number of industry
awards
at the show. GPUs were highlighted in two
Gordon Bell1,
2 awards. The best
student paper
went the way of Tokyo and Purdue Universities who collaborated on a new interface to
make parallel programming on the GPU even more accessible. And perhaps most
exciting, we saw some major organizations receiving honors for their work with GPUs,
including ,
SchlumbergerCitadel Investment
Group and Weta Digital.

Some Years in Review for my Year in Review - while writing this, a couple of
other year in review articles caught my eye, and included some quotes that I thought
would make a fitting end to this recap.

HPCwire released their biggest trends of the year podcast last week and
pronounced GPU Computing as the #1 Trend of the Year. They commented:

"This year, it (GPU Computing) hit the mainstream, deployed by all the major vendors."

They also added that:

"If NVIDIA hadn't been there, this wouldn't have happened. AMD was only lukewarm
about this. NVIDIA put energy and money into it. They changed the trajectory of
GPU computing, without a doubt. NVIDIA CUDA made this possible."

Another article recently appeared on O'Reilly Media, who
produce a wealth of books, online services, magazines, research, and conferences for
the technical computing community. Their summary was that GPUs coupled with CPUs is
the architecture of choice for the processing of computationally heavy data

"You won't get the processing power you need at a price you want just by enabling
traditional multicore CPUs; you need the dedicated computational units that GPUs
provide."

We couldn't agree more :)

And so 2011 is upon us. From everyone in the NVIDIA Tesla and CUDA teams, we wish
you a happy and successful New Year.